Manjaro is a sweet Arch-based Linux distribution, and it has the fans to prove it. Manjaro is designed to take Arch to new heights of user-friendliness, and it succeeds quite well. Of course, there are always those who believe everything can be improved, which is why a small team of developers decided to fork Manjaro and create Elegance. The beauty of Elegance isn't in the UI, although the developers have made Cinnamon look pretty good.
On December 19, 2025, Cursor acquired Graphite for more than $290 million. CEO Michael Truell framed the move simply: code review is taking up a growing share of developer time as the time spent writing code keeps shrinking. The message is clear. AI coding tools have largely solved the generation speed. Now the industry is betting that review is the next constraint to break.
There is no flow state that comes from building a Mac app using AI with Claude Code. If you've ever managed programmers, you know what using Claude Code feels like. It is an enormous force multiplier, but you're going to spend most of your time cajoling and correcting, and some of your time trying to chart your way out of AI-generated chaos.
The COVID-19 pandemic has emptied the world's offices, sending most of us to work from home for the foreseeable future and facilitating a mass migration to the digital workplace. But will it also jumpstart the arrival of our non-human colleagues? AMELIA ("AI + ME = AMELIA") from IPsoft can read 300 pages in 30 seconds, comprehend multiple languages (including logic and context), and is installed in more than 500 banks, insurance companies, and retail giants.
When I work on something, whether it's at Interfere or my personal projects, I like to experiment a lot. Design engineering is a lot about trial and error, and I often spend hours trying to find the "this feels right" moment. This is where AI helps. Instead of spending hours on a concept that I'm unsure of, I try that concept out in a matter of minutes, and throw it away if it doesn't feel right.
Key Issues: General UI Lag: Clicking between slides or menus is delayed and often "hangs." Audio/Text Desync: When adjusting text boxes to narration, the audio continues to play but the UI/subtitles freeze for several seconds. Overall Stability: The app feels unresponsive and struggles to handle the project size. What can I do to resolve this issue?Because currently the app is unusable for professional production.
One of my oldest open-source projects - Bob - has celebrated 15 a couple of months ago. Bob is a suite of implementations of the Scheme programming language in Python, including an interpreter, a compiler and a VM. Back then I was doing some hacking on CPython internals and was very curious about how CPython-like bytecode VMs work; Bob was an experiment to find out, by implementing one from scratch for R5RS Scheme.
A trade-free operating system, that's what Tromjaro means. But what does a trade-free operating system have to offer? Well, it means a lot, especially if you're tired of the imbalance between those who have versus those who want. From the developers' perspective, this "trade-free" OS wants nothing from its users, such as no data collection and no demands for attention: "This is the purest form of free and the most honest one."
I'll be talking about holistic engineering or the practice of factoring in your technical decisions, designs, strategies, all the non-technical factors that are actually forces that influence your organic socio-technical problem space. As much as you can see in this canyon how natural forces have influenced the shape of the earth, so you can see the color. You can see all the different layers.
A little bit about myself. In my previous life, I was staff platform engineering. I focused a lot of development engineering and everything that basically was the sociotechnical aspect of our technical work. I recently was working as a CTO and co-founder of a startup, and nowadays I'm just doing advisory roles and a little bit of consulting while trying to think about the next big thing. Yes, so happy to be talking with you, Shane.
Software engineering didn't adopt AI agents faster because engineers are more adventurous, or the use case was better. They adopted them more quickly because they already had Git. Long before AI arrived, software development had normalized version control, branching, structured approvals, reproducibility, and diff-based accountability. These weren't conveniences. They were the infrastructure that made collaboration possible. When AI agents appeared, they fit naturally into a discipline that already knew how to absorb change without losing control.
Over the past decade, software development has been shaped by two closely related transformations. One is the rise of devops and continuous integration and continuous delivery (CI/CD), which brought development and operations teams together around automated, incremental software delivery. The other is the shift from monolithic applications to distributed, cloud-native systems built from microservices and containers, typically managed by orchestration platforms such as Kubernetes.
In total I probably spent around 45 minutes actively with it. It worked for around 3 hours while I was watching, then another 7 hours alone. This post is a recollection of what happened and what I learned from it. All prompting was done by voice using pi, starting with Opus 4.5 and switching to GPT-5.2 Codex for the long tail of test fixing.
When I joined Google ~14 years ago, I thought the job was about writing great code. I was partly right. But the longer I've stayed, the more I've realized that the engineers who thrive aren't necessarily the best programmers - they're the ones who've figured out how to navigate everything around the code: the people, the politics, the alignment, the ambiguity.
A recent commit to integrate and enable the JXL decoder means that future releases of Google Chrome and other Chromium-based browsers will include code to process and present JXL images. The format's supporters argue JXL can be used to recompress existing JPEG images without loss so they're 20 percent smaller, which alone would represent a significant bandwidth saving for websites and content delivery networks.
Decorators are a concept that can trip up new Python users. You may find this definition helpful: A decorator is a function that takes in another function and adds new functionality to it without modifying the original function. Functions can be used just like any other data type in Python. A function can be passed to a function or returned from a function, just like a string or integer.
Software development used to be simpler, with fewer choices about which platforms and languages to learn. You were either a Java, .NET, or LAMP developer. You focused on AWS, Azure, or Google Cloud. Full-stack developers learned the intricacies of selected JavaScript frameworks, relational databases, and CI/CD tools. In the best of times, developers advanced their technology skills with their employer's funding and time to experiment. They attended conferences, took courses, and learned the low-code development platforms their employers invested in.
For an operating system that already enjoys rock-solid security, imagine taking that even further to create an operating system that is almost unbreakable. With immutability, Linux distributions mount the core of the system as read-only, which means the contents of those directories cannot be changed. So, if you were to unwittingly install a piece of malicious software, it would not be able to make changes to directories like /bin, /sbin, /usr, /lib, and /etc. That's some pretty high security there.
Every few months, the developer tool hype machine finds a new hero. In 2023, it was GitHub Copilot, the AI pair programmer that made autocomplete feel like magic. In 2024, the vibe shifted to Cursor and the new class of AI-first editors. And now, at least on X, Google's "agent-first" Antigravity is being pitched as the next inevitable thing. Meanwhile, the model layer keeps whiplashing.
In today's episode, I will be speaking with Somtochi Onyekwere, software engineer at Fly.io organization. We will discuss the recent developments in distributed data systems, especially topics like eventual consistency and how to achieve fast, eventually consistent replication across distributed nodes. We'll also talk about the conflict-free replicated data type data structures, also known as CRDTs and how they can help with conflict resolution when managing data in distributed data storage systems.
Many people describe localization services as 'translation plus cultural adaptation.' While this is technically correct, it is not very helpful in practice. This definition misses the actual work, the decisions teams face, and the reasons some localization projects quietly succeed or fail. This article explains how localization services actually work for software, web platforms, and global content and gives a practical model for teams who want to know what they are getting into when they decide, 'We need localization.'
